Project Overview
This project analyzes restaurant sales data using SQL to identify key revenue drivers, understand customer ordering behavior, and examine sales trends across products, categories, and time. The analysis helps uncover patterns that can support better business decisions for menu optimization, promotions, and operational planning.
The dataset consists of two tables:
menu_items – Contains information about the restaurant menu, including item name, category, and price. order_details – Contains transactional data such as order ID, order date, order time, and the menu item ordered.
The dataset includes 32 menu items, 12,234 ordered items, and 5,370 distinct customer orders.
Several SQL queries were used to explore the data, including:
- Total revenue generated by each product
- Category-wise sales performance
- Monthly sales trends
- Hourly demand analysis
- Top revenue-generating items during peak hours
- Average number of items per order
- Revenue contribution by food category
The analysis shows that Korean Beef Bowl is the highest revenue-generating menu item. Italian cuisine contributes the largest share of total revenue, followed by Asian cuisine, and together they account for more than 60% of total sales. Sales peak during lunch and dinner hours, while mornings and late nights show lower demand. Customers order an average of 2.27 items per order, indicating frequent multi-item purchases.
- SQL (MySQL)
- Data aggregation
- Window functions